Various imaging techniques in two or more dimensions are now central within many fields of technology. For example, satellite images are used as originals or in processed form for mapping, in demographic, economic and environmental analyses, as well as within community planning. Three-dimensional images is for instance achieved through various medical imaging techniques, and are used to analyze the human body, as support for surgeons during surgery, and so forth, but also within for example the nanotechnological field. Collected image data is analyzed and used in numerous applications to control the behaviour of robots and other technical equipment, with the aim of navigating through space, selecting and handling objects and interacting with other technology as well as with people. Furthermore, two-dimensional imaging techniques are used within microscopy.
In general, the information contained in such an image requires interpretation before use. The goal of such interpretation is typically to identify the structural components in the image, such as an object against a background; different fields that are delimited one to another or volumes of different colour intensity, structure or which are distinct as compared to each other in other ways; or deviating elements. Examples include identifying roads, housing, forest, farm land, etc. from satellite images; distinguishing faces in a photography depicting human beings; separating various types of tissue in a three-dimensional NMR image of a person; and identification of material deviations based upon a photography of a manufactured detail.
It is often for cost reasons desirable to achieve an automatic interpretation of an image. One way to carry out such interpretation starts out from a digital image in two or more dimensions, built up from a number of individual pixels. Each pixel is then associated with one certain respective class of pixels, selected among a number of such classes designed to represent a certain pixel type. When all pixels have been associated with a respective class, an inventory can be made of each class, so as to obtain a collected picture of where in the image pixels of a certain type occur. Herein, such method is denoted a “classification”. Hence, a certain class can for example represent “road”, “muscle tissue” or “material defect”.
Typically, classification techniques are used to locate objects and borders, such as lines, curves, fields, etc., in an image.
Several attempts have been made to achieve a method for automatically performing various useful classifications of images where the knowledge of the image contents is limited before the start of the classification.
For instance, a method has been proposed in which a movable “window” is swept across the image in an attempt to classify a pixel located in the centre of the window to a certain class identity by studying the centre pixel's surrounding pixels and using statistical methods (kernel-based segmentation). For some types of images, such methods can be efficient, but the result of the classification is often very scattered, with classes comprising pixels from many different parts of the image. The result is that it is difficult to obtain useful information from the classification without large amounts of manual work.
An automatic classification of an image has also been proposed with parallel consideration to all pixels, in an iterative method (window-independent classification). One example of an algorithm which can be used in such method is a cluster analysis of K-means type. Even such methods often result in scattered classifications when used to classify digitally stored images.
In the article “Automated Segmentation of MR Images of Brain Tumors”, Kaus, Michael R., et al., Radiology 2001; 218:586-591, an iterative classification of a three-dimensional MR-reproduction of a human skull is disclosed. The classification is performed iteratively, with the help of among other things local segmentation strategies and a distance transform which calculates the distance between a certain voxel (a three-dimensional pixel) and a certain class, and also on the basis of information regarding greyscale intensity of the voxels taking part of the reproduction.
The majority of the steps making up such method must be carried out manually in order to achieve sufficient reliability of the finally classified result. Additionally, a comparatively solid knowledge of the object is required before the classification is started, for example in the form of a comparative image illustrating a “normal case” or the like.
The present invention solves the above described problems.